In this report, we analyzed the GHG emissions in Redwood City. We analyzed energy consumption data from 2013 to 2019 in terms of building emissions and vehicle emissions. Based on data from previous years, GHG emissions are forecast from 2020 to 2050.

Part1 Analysis of vehicle emissions

For vehicle emissions, we used LODES 2013 to 2019 data to calculate commute emissions in Redwood City as both an origin and destination.

To get the geographical scope of the study, we first use zctas() to get the geographic information of each zip code area, and then use places() to filter to the boundary of redwood city. If the centroid of a zip code area is within the Redwood City boundary, then this zip code area will be regarded as a sub-region within the Redwood City. By calculation, we obtained three sub-regions within Redwood City: 94061, 04063, 94065. These three areas will serve as the entire geographical scope of the Redwood City.

In order to obtain the route of CBG - to ZIP level, for these three zip code areas (94061, 04063, 94065), we created a dataframe to save the block and CBG information corresponding to each zip code area.

ZIP/CBG/Block conversion
GEOID10 cbg zipcode
060816107002010 060816107002 94061
060816108002006 060816108002 94061
060816107002004 060816107002 94061
060816110004017 060816110004 94061
060816107002002 060816107002 94061

After we got LODES data, we filtered out routes that origin and destination are within Redwood City blocks. We assumed that these short commutes would not have a significant impact on the total vehicle emissions. In this way, routes could be divided into two categories, the first category “inbound”, that is, from the outside of Redwood City to a destination within the boundary; the second category “inbound”, that is, from Redwood City to an external destination.

Next, we did a transformation on all routes. To be specific, whether the route is outbound or inbound, the endpoint within the Redwood City would be assigned to a location that was the same as the centroid of the zip code area; the endpoint outside the Redwood City would be assigned to a location that was the same as the centroid of the CBG area.

After the above-mentioned series of processes and transformations, we used mb_directions to obtain all the routes details. We created a dataframe to store the distance, duration and geographic information of each route. So far, we got the distance of each route, and the distance could be used to calculate VMT. VTM are important indicator of GHG emissions, so that we can convert vehicle miles to emissions. The map below shows all the routes from or to Redwood City.

Notice that all routes are within California. This is another assumption we made. We believe that all trips within the geographic scope of California are within a reasonable driving distance. If people want to go to areas outside California, we believe that they are more likely to choose other means of transportation, such as airplanes.

Blocks with trip data as a percentage of all blocks in California
value perc
0.800353509225729 * 100%

The above table shows that about 80% of the blocks in CA have trip data related to Redwood City.

The next step is to get the necessary data to calculate GHG emissions from commuter vehicles. So far we still need to get the following information: 1. driving patterns and their corresponding percentage (drove aline / In 2-person carpool / In 3-or-more-person carpool) 2. Number of visits per year

For driving patterns, we calculate the percentage of each type of driving pattern by using the CENSUS data acs/acs5 B08134. For number of visits, we assume that there are 261 working days per year, and the number of visits equals to S000 * 261, where S000 refers to the number of employeed people. Therefore: \[number\ of \ vehicles = visits * percentage\ pattern1 + \frac{visits * percentage\ pattern2}{2} + \frac{visits * percentage\ pattern3}{3}\] \[VMT = number\ of \ vehicles * distance * 2\] Note that when calculating the VMT, we multiplied the result by two because we assumed that each trip was a round trip.

Having obtained the VMT data, we used EMFAC to estimate the GHG emissions of the vehicles during starting and running process.

Finally, the GHG calculation formula is as follows: \[GHG = MTCO2\ Running\ Exhaust * VMT + MTCO2\ Start\ Exhaust * 2 * trips \]

The graph above shows Redwood City’s total vehicle GHG emissions from 2014 to 2019. From 2013 to 2015, GHG emissions showed a donwnward trend, reached the lowest value in 2015, and then increased for 5 consecutive years.

Part2 Analysis of building emissions

2.1 PG&E 2013 to 2019, residential and commercial electricity and gas usage

The above plot shows the pounds of CO2 per MHh. From 2013 to 2019, the continuous decrease in the value means that the greenhouse gas emissions from electricity consumption have been decreasing year by year, and have approached to zero by 2019.

Due to the increasing renewable content in electricity generation, the sharp divergence in carbon footprint between gas and electricity use can help to reduce GHG in a great deal.

We drew the above bar chart to visualize the trend of the total energy used (GBTU) of the four customer classes in Redwood City from 2013 to 2019. As can be seen, the energy consumption of Elec- commercial, Elec- Residential and Gas- Commercial have been flat (or slightly fluctuated) over 7 years, while Gas- Commercial has a slight upward trend.

This chart visualized the trend of the total CO2 emissions of the four customer classes in Redwood City from 2013 to 2019.

As can be seen, the energy consumption of Elec- commercial, Elec- Residential and Gas- Commercial have been flat (or slightly fluctuated) over 7 years, while Gas- Commercial has a slight upward trend. Both commercial and residential electricity consumption approached zero in 2019. This is due to the increased proportion of renewable energy used in electricity generation. Regarding the CO2 emissions from Gas, Gas-Residential’s CO2 emissions remained flat, while Gas-Commercial’s CO2 emissions rose slightly.

This can be caused by a number of factors, such as population growth, job growth, climate change that leads to more needs on heating/cooling, and more. In the third part of the report, we will analyze factors that may contribute to overall GHG emissions and based on them to make projections of future GHG emissions.

2.2 residential energy use per resident and commercial energy use per job

In this part, we used Census population data to estimate residential energy use per resident, and LODES WAC data to estimate commercial energy use per job.

We used acs/acs5 B01001 data and got the total population in Redwood City from 2013 to 2019. The table shows that from 2013 to 2019, the total population in Redwood City grew steadily.

total population in Redwood City, from 2013 to 2019
YEAR total_pop_rwc
2013 86515
2014 88245
2015 89898
2016 92161
2017 93915
2018 94109
2019 94467

Accordingly, we divided TOTALKBTU by total population to get residential energy use per resident. Except for the high gas consumption in 2017, the electricity consumption and gas consumption of residential building did not changed significantly. Moreover, the Electricity consumption is about half of Gas consumption.

We divided TOTALKBTU by number of jobs to get commercial energy use per job. Compared with energy use per resident, the total energy consumption of commercial building is significantly higher. And another significant difference is that the electricity consumption of commercial building is much higher than that of gas consumption, while the gas consumption of residential building is higher.

2.3 KBTU/resident/HDD, KBTU/resident/CDD, KBTU/job/HDD, KBTU/job/CDD

To further normalize the energy consumption data, we used the Cal-Adapt Degree Day tool to collect HDDs and CDDs in Redwood City from 2013 to 2019. These two table shows the CanESM2 (Average) values of HDD and CDD.

HDD,CanESM2 (Average) values
YEAR CanESM2
2013 1949.0319824219
2014 2178.185546875
2015 2098.212890625
2016 1826.0477294922
2017 2030.1068115234
2018 2107.6755371094
2019 2280.3342285156
CDD,CanESM2 (Average) values
YEAR CanESM2
2013 594.2301025391
2014 502.7012023926
2015 594.9689941406
2016 486.4707336426
2017 615.2528076172
2018 701.5762329102
2019 693.5844116211

A cooling degree day (CDD) is the number of degrees by which a daily average temperature exceeds a base temperature and may therefore require additional energy for space cooling, and a heating degree day (HDD) is the number of degrees by which a daily average temperature is below a base temperature and may therefore require space heating. They have an important impact on the consumption of gas for heating and the use of electricity for cooling, respectively.

After taking HDD and CDD into consideration, we got the above table. The value of electricity use per job of commercial building is much higher than the values of the other three categories. This is in line with the conclusion we got before considering CDD and HDD. Also, we can find that the total building energy consumption is decreasing year by year, although the electricity consumption of KBTU_job_CDD fluctuated to a certain extent.

In addition, we found that after considering CDD and HDD, the proportion of residential gas consumption and commercial gas consumption to the total energy consumption decreased a lot. This may because that the amount of gas used for heating changed due to climate change.

Part3 Reflections on the overall results

3.1 total vehicle and building emissions

This plot stacks total vehicle and building emissions in Redwood City year-by-year from 2013 to 2019. We kept the building emission subcategories separate, in order to see more clearly what’s going on with the subcategories.

As is shown in this plot, the vehicle emission kept flat or slightly increased from 2013 to 2019; commercial and residential building electricity emission had dropped sharply to a level close to 0; and commercial and residential building gas emission had shown a slight upward trend. In general, total GHG emission in redwood city was decreasing year by year. This was mainly due to a significant reduction in electricity emissions.

3.2 factors that contribute to the overall GHG estimates

In this subsection, we made prediction for building GHG emissions and vehicle GHG emissions respectively.

For building GHG emission, we considered the following 4 factors for prediction: population growth, job growth, HDD, CDD. For each of these 4 factors, we took the 2013-2019 data as input to the linear regression model, and made prediction for the value in 2020, 2025, 2030, 2035, 2040, 2045, 2050.

Taking job growth and population growth as examples, below is the summary of the linear regression model.

Job growth:

## 
## Call:
## lm(formula = JobResidents ~ year, data = job_rwc)
## 
## Residuals:
##       1       2       3       4       5       6       7 
##   602.3  -966.2  2549.3 -1236.1 -3532.6  1793.9   789.5 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -3989733     862821  -4.624  0.00571 **
## year            1992        428   4.653  0.00557 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2265 on 5 degrees of freedom
## Multiple R-squared:  0.8124, Adjusted R-squared:  0.7749 
## F-statistic: 21.65 on 1 and 5 DF,  p-value: 0.005567

Population growth:

## 
## Call:
## lm(formula = Population ~ year, data = pop_rwc)
## 
## Residuals:
##       1       2       3       4       5       6       7 
## -1328.4  -163.0  1441.4   278.9  1169.3  -155.3 -1242.9 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -1583697.9   447002.0  -3.543   0.0165 *
## year             827.6      221.7   3.732   0.0135 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1173 on 5 degrees of freedom
## Multiple R-squared:  0.7359, Adjusted R-squared:  0.6831 
## F-statistic: 13.93 on 1 and 5 DF,  p-value: 0.01354

After doing the same process on HDD and CDD, we obtained a dataframe that have prediction result of each factors.

forecasting result of factors related to GHG emissions
year jobs population CDD_value HDD_value
1 2013 19687.00 80875.00 594.2301025391 1949.0319824219
2 2014 20110.00 82868.00 502.7012023926 2178.185546875
3 2015 25617.00 85300.00 594.9689941406 2098.212890625
4 2016 23823.00 84965.00 486.4707336426 1826.0477294922
5 2017 23518.00 86683.00 615.2528076172 2030.1068115234
6 2018 30836.00 86186.00 701.5762329102 2107.6755371094
7 2019 31823.00 85926.00 693.5844116211 2280.3342285156
11 2020 33025.00 87996.43 723.5003051758 1903.6209716797
21 2025 42982.32 92134.29 682.8156738281 2179.6267089844
31 2030 52939.64 96272.14 1018.3406982422 1890.5731201172
41 2035 62896.96 100410.00 944.2271728516 1960.9119873047
51 2040 72854.29 104547.86 948.0750732422 1923.6513671875
61 2045 82811.61 108685.71 1105.8905029297 2055.9016113281
71 2050 92768.93 112823.57 1020.2711791992 1716.9244384766

As shown in the figure, after the blue dotted line, i.e. after 2019, we showed the predicted results of gas GHG emission in Redwood City. It is needed to mention that we did not make a prediction for the electricity GHG emission, because starting from 2019, the electricity GHG emission of the subsequent years are all 0. As can be seen from this figure, the average CO2 emissions (per job for commercial building, per resident for residential building) are trending downward. This is related to population growth and job growth.

Next, we moved on to forecasting the GHG emission for vehicles. We made the following assumptions: 1. For gasoline vehicles, we assume that the percentage of trips decrease by 1% every year, percentage of miles decrease by 1% every year; 2. For electricity vehicles, we assume that the percentage of trips increase by 1% every year, percentage of miles increase by 1% every year; 3. For Diesel vehicles, the percentage of trips and percentage of miles keep the same; 4. We assumed a linear increase in VMT and number of visits with year and used a linear regression model to predict.

The prediction results show that the GHG emission of diesel and gasoline vehicles will continue to rise, and the GHG emission of Diesel vehicles increase rate are larger. The total GHG emission in 2050 will be as twice as that in 2013. In order to reduce vehicle GHG emissions, we should reduce the proportion of diesel and gasoline vehicles and promote renewable energy vehicles.

Based on the analysis and predictions above, from my personal perspective, these factors will drive future changes in Redwood City’s transportation and building GHG footprint.

building GHG footprint
factor1 population Population growth will lead to an increase in total GHG emissions. It has the greatest impact on GHG emissions from residential buildings, especially for gas GHG emissions.
factor2 jobs job growth has the greatest impact on GHG emissions from commercial buildings. However, since commercial building consumes a larger proportion of electricity and electricity does not generate GHG emissions, job growth has a smaller impact on total GHG emissions than population growth.
factor3 HDD climate change in winter (colder than before) lead to an increasing demand for heating, resulting in greater gas consumption
factor4 CDD global warming lead to an increasing demand for cooling, resulting in greater electricity consumption. Electricity does not generate GHG emissions, so it has a smaller impact on total GHG emissions
vehicle GHG footprint
factor1 proportion of new energy vehicle The rise in the number of new energy vehicles, such as electricity vehicles, will lead to a significant drop in total vehicle GHG emissions. If the number of diesel and gasoline vehicles does not decrease in the future, the increase in population job growth will lead to an increase in VMT, which will result in a dramatic increase in total GHG emissions.
factor2 driving pattern Carpool can reduce the total trip miles and total visits, which can well alleviate the problem of the total increase in vehicle GHG emission. The increase in the proportion of driving alone will have a negative impact on GHG emission
factor3 VMT The number of visit is caused by the growth of population and jobs. These are unavoidable. Therefore, we can solve this problem from the perspective of reducing the total mileage, such as using public transportation for long-distance commuting.

3.3 GHG footprint allocation

Currently, for scope 3 emission allocating, the main challenges are as follows:

  1. Emission ownership cannot be clearly defined, most companies focus their effort on scope 1 and scope 2, because the lack of relevant policies for scope 3 emission responsibility.

  2. Lack of a unified measurement method. The lack of coordination between the stakeholders in the supply chain has resulted in a large number of different calculation method for scope 3, leading to confusion among these stakeholders about which method to choose.

In order to solve these two problems, I think efforts should be made from the following perspectives:

  1. Clarify the responsibilities of upstream and downstream stakeholders in supply chain for scope 3 emissions. The supply chain often has these stakeholders: supplier, manufacturer, wholesaler, retailer and buyer. For two stakeholders in the supply chain, their relationship is more distant as the number of entities between them increase, which mean the upper-stream entity has less responsibility for scope 3 GHG generated by downstream entities. For two adjacent stakeholders, the upper-stream entity should assume regulatory responsibility, because the GHG emission generated by the downstream entity belongs to the scope 3 emission of this upper-stream entity.

  2. Formulate a unified calculation method,to be more specific, a scope 3 calculation guide that is reasonable and suitable for the economic and political conditions of the Bay Area.

  3. Urge upstream entities to take the initiative to be responsible for scope 3 emissions, and take some measures such as use recycled materials, reduce production waste, engage key suppliers on improving the carbon footprint, cooperate with sustainable suppliers, provide consumers with environmentally friendly consumption options, etc.